In LapSRN [1]}, curriculum learning is applied to progressively reconstruct the sub-band residuals of high-resolution images. In ProSR [2]}, each level of the pyramid is gradually blended in to reduce the impact on the previously trained layers and the training pairs of each scale are incrementally added. In SRFBN [3]}, the curriculum learning strategy is applied to solve the complex degradation tasks, where targets of different difficulties are ordered to learn it progressively. With the help of curriculum learning, complex problems can be decomposed into multiple simple tasks, hence accelerating model convergence and obtaining better reconstruction results.
